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About this paper
Zijie Xu, Zihan Huang, Yiting Dong, Kang Chen, Wenxuan Liu, Zhaofei Yu
The authors introduce Cross-Step Residual Potential Initialization (CRPI), a gradient-free method for converting trained artificial neural network policies into spiking neural networks for continuous-control tasks. It carries scaled residual membrane potential between decisions to reduce correlated action errors and state drift. In six visual control tasks, average performance ratio rises from 69.60% to 91.84%, below the ANN reference.
Summary by alphaXiv.
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